The primary goal of artificial intelligence research is to develop a
machine capable of learning to solve disparate real-world tasks
autonomously, without relying on specialized problem-specific
inputs. This dissertation suggests that such machines are
realistic: If No Free Lunch theorems were to apply to all real-world
problems, then the world would be utterly unpredictable. In
response, the dissertation proposes the information-maximization
principle, which claims that the optimal optimization methods make
the best use of the information available to them. This principle
results in a new algorithm, evolutionary annealing, which is shown
to perform well especially in challenging problems with irregular
structure.